Article
Real-Time Threat Detection Using Stream Analytics and Deep Learning
The increasing volume and speed of network traffic, along with the growing sophistication of cyber threats, have made real-time intrusion detection essential for modern digital infrastructures. Traditional security systems often depend on static rules or offline analysis, which limits their ability to detect new or rapidly evolving cyberattacks. This paper proposes a hybrid architecture that combines stream processing frameworks with deep learning models to enable real-time threat detection from network logs. The system uses Apache Kafka for efficient log ingestion and Apache Flink for real-time stream processing and analytics. Deep learning models, including Long Short-Term Memory (LSTM) networks and one-dimensional Convolutional Neural Networks (1D CNN), are applied for anomaly detection and threat identification. The proposed approach is evaluated using benchmark datasets such as CIC-IDS 2017 and UNSW-NB15. Experimental results show that the system can detect network threats with high accuracy and low latency while maintaining scalability under highthroughput conditions. The architecture is therefore suitable for deployment in real-world operational environments where fast and accurate threat detection is crucial.
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